Evolutionary optimization in dynamic fitness landscape environments

Abstract

Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm.
Genetic diversity is crucial to provide the necessary adaptability of the algorithm to unexpected changes.
Two key concepts to maintain genetic diversity in the population are incorporated to the algorithm and proposed here: macromutation operators and random immigrants.
The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determine the algorithm ability to face changes and dimensional or multimodal scalability in the functions.
The effectiveness and limitations of the proposed algorithm in diverse scenarios of a dynamic environment is discussed from results empirically obtained.